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Reinforcement learning can provide effective reasoning for sequentialdecision-making problems with variable dynamics. Such reasoning in practicalimplementation, however, poses a persistent challenge in interpreting thereward function and the corresponding optimal policy. Consequently,representing sequential decision-making problems as probabilistic inference canhave considerable value, as, in principle, the inference offers diverse andpowerful mathematical tools to infer the stochastic dynamics whilst suggestinga probabilistic interpretation of policy optimization. In this study, wepropose a novel Adaptive Wasserstein Variational Optimization, namely AWaVO, totackle these interpretability challenges. Our approach uses formal methods toachieve the interpretability for convergence guarantee, training transparency,and intrinsic decision-interpretation. To demonstrate its practicality, weshowcase guaranteed interpretability with an optimal global convergence rate insimulation and in practical quadrotor tasks. In comparison withstate-of-the-art benchmarks including TRPO-IPO, PCPO and CRPO, we empiricallyverify that AWaVO offers a reasonable trade-off between high performance andsufficient interpretability.

Probabilistic Constrained Reinforcement Learning with Formal Interpretability